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Human Feedback and Eval Paper Explorer

A focused feed for RLHF, preference data, rater protocols, agent evaluation, and LLM-as-judge research. Every paper includes structured metadata for quick triage.

Total papers: 9 Search mode: keyword Shortlist (0) RSS

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Match reason: Matches selected tags (Long Horizon, Demonstrations).

Score: 58% High protocol signal Freshness: Warm Status: Ready
Demonstrations Human EvalLlm As Judge Long Horizon General
  • LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…
  • We introduce AgentHER, a framework that recovers this lost training signal by adapting the Hindsight Experience Replay (HER; Andrychowicz et al., 2017) principle to natural-language agent trajectories for offline data augmentation.
Open paper
IROSA: Interactive Robot Skill Adaptation using Natural Language

Markus Knauer, Samuel Bustamante, Thomas Eiband, Alin Albu-Schäffer, Freek Stulp, João Silvério · Mar 4, 2026

Citations: 0

Match reason: Matches selected tags (Long Horizon, Demonstrations).

Score: 55% Moderate protocol signal Freshness: Warm Status: Ready
Demonstrations Long Horizon General
  • We demonstrate the framework on a 7-DoF torque-controlled robot performing an industrial bearing ring insertion task, showing successful skill adaptation through natural language commands for speed adjustment, trajectory correction, and…
Open paper
MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation

Chengshu Li, Mengdi Xu, Arpit Bahety, Hang Yin, Yunfan Jiang, Huang Huang · Oct 21, 2025

Citations: 0

Match reason: Matches selected tags (Long Horizon, Demonstrations).

Score: 53% Moderate protocol signal Freshness: Cold Status: Ready
Demonstrations Simulation Env Long Horizon General
  • Imitation learning from large-scale, diverse human demonstrations has been shown to be effective for training robots, but collecting such data is costly and time-consuming.
  • This challenge intensifies for multi-step bimanual mobile manipulation, where humans must teleoperate both the mobile base and two high-DoF arms.
Open paper
Watch and Learn: Learning to Use Computers from Online Videos

Chan Hee Song, Yiwen Song, Palash Goyal, Yu Su, Oriana Riva, Hamid Palangi · Oct 6, 2025

Citations: 0

Match reason: Matches selected tags (Long Horizon, Demonstrations).

Score: 53% Moderate protocol signal Freshness: Cold Status: Ready
Demonstrations Long Horizon General
  • Computer-using agents (CUAs) must plan task workflows across diverse and evolving applications, yet progress is limited by the lack of large-scale, high-quality training data.
  • We present Watch & Learn (W&L), a framework that converts readily available Internet videos of human computer use into executable UI trajectories at scale.
Open paper
Efficient Agent Training for Computer Use

Yanheng He, Jiahe Jin, Pengfei Liu · May 20, 2025

Citations: 0

Match reason: Matches selected tags (Long Horizon, Demonstrations).

Score: 53% Moderate protocol signal Freshness: Cold Status: Ready
Demonstrations Long Horizon Coding
  • We introduce PC Agent-E, an efficient agent training framework that significantly reduces reliance on large-scale human demonstrations.
  • Trained on these enriched trajectories, our PC Agent-E model achieved a remarkable 141 relative improvement, and even surpassed the Claude 3.7 Sonnet by 10% in relative terms on WindowsAgentArena-V2, an improved benchmark we also released.
Open paper
Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning

Ruize Zhang, Sirui Xiang, Zelai Xu, Feng Gao, Shilong Ji, Wenhao Tang · May 7, 2025

Citations: 0

Match reason: Matches selected tags (Long Horizon, Demonstrations).

Score: 53% High protocol signal Freshness: Cold Status: Ready
Demonstrations Automatic Metrics Long Horizon General
  • The task is turn-based, multi-agent, and physically grounded, posing significant challenges due to its long-horizon dependencies, tight inter-agent coupling, and the underactuated dynamics of quadrotors.
Open paper
Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning

Yihe Deng, I-Hung Hsu, Jun Yan, Zifeng Wang, Rujun Han, Gufeng Zhang · Oct 29, 2025

Citations: 0

Match reason: Matches selected tags (Long Horizon, Demonstrations).

Score: 50% Moderate protocol signal Freshness: Cold Status: Ready
Demonstrations Long Horizon Coding
  • Beyond reasoning benchmarks, SRL generalizes effectively to agentic software engineering tasks, establishing it as a robust and versatile training framework for reasoning-oriented LLMs.
Open paper

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